Explainability for Fault Detection System in Chemical Processes
This work addresses interpretability for fault detection in chemical processes, but it is incremental as it applies existing XAI methods to a known benchmark.
The study applied and compared two XAI methods, Integrated Gradients and SHAP, to explain fault diagnosis decisions by an LSTM classifier in the Tennessee Eastman Process, finding that SHAP was sometimes more informative for identifying fault root causes.
In this work, we apply and compare two state-of-the-art eXplainability Artificial Intelligence (XAI) methods, the Integrated Gradients (IG) and the SHapley Additive exPlanations (SHAP), that explain the fault diagnosis decisions of a highly accurate Long Short-Time Memory (LSTM) classifier. The classifier is trained to detect faults in a benchmark non-linear chemical process, the Tennessee Eastman Process (TEP). It is highlighted how XAI methods can help identify the subsystem of the process where the fault occurred. Using our knowledge of the process, we note that in most cases the same features are indicated as the most important for the decision, while insome cases the SHAP method seems to be more informative and closer to the root cause of the fault. Finally, since the used XAI methods are model-agnostic, the proposed approach is not limited to the specific process and can also be used in similar problems.